Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Регресионен дизайн с прекъсване в образователните изследвания× | Съгласуване по показател на склонност× | |
|---|---|---|
| Област≠ | Причинно-следствено заключение | Статистика за изследвания |
| Семейство≠ | Regression model | Process / pipeline |
| Година на възникване≠ | 1960 (origination); 1999-2010 (education economics canon) | 1983 |
| Създател≠ | Thistlethwaite & Campbell (1960); popularized in education economics by Angrist & Lavy (1999), Lee & Lemieux (2010) | Paul Rosenbaum and Donald Rubin |
| Тип≠ | Quasi-experimental causal inference | Method |
| Основополагащ източник≠ | Lee, D. S., & Lemieux, T. (2010). Regression discontinuity designs in economics. Journal of Economic Literature, 48(2), 281-355. DOI ↗ | Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55. DOI ↗ |
| Други названия≠ | RDD in education, education RD design, sharp RDD education, score-cutoff design | PSM, propensity score weighting, covariate balance |
| Свързани≠ | 5 | 3 |
| Резюме≠ | Regression discontinuity design (RDD) in education research exploits a score-based eligibility cutoff — such as a test score threshold, GPA requirement, or age cutoff — to estimate the causal effect of a program, intervention, or policy on student or school outcomes. Units just below and just above the cutoff are treated as near-randomly assigned, enabling credible causal inference without a randomized trial. | Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias. |
| ScholarGateНабор от данни ↗ |
|
|